| # AGENTS.md | |
| - Do not preserve backward compatibility. Remove obsolete paths instead of adding compatibility layers, fallbacks, or migrations. | |
| - Choose the simplest implementation that fully meets the current requirements. Avoid speculative abstractions, configuration, and indirection. | |
| - Grow the system in layers. Start from the smallest version that works end to end, and add each new capability on top of a product that already works. Never trade a working product for unfinished complexity. | |
| - Keep components modular and concerns clearly separated. | |
| - Prefer established, well-maintained libraries when they reduce overall complexity or improve reliability. Do not reimplement common functionality without a clear reason. | |
| - Lean on the dependencies already in the project before writing your own implementation or adding packages. Do not assume a library lacks a capability without checking its documentation and types. | |
| - Make architectural decisions for the long term. Do not accept a stopgap that only works for now an |
The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.
The correct way of creating a private frok by duplicating the repo is documented here.
For this assignment the commands are:
git clone --bare git@github.com:usi-systems/easytrace.git
All rules and guidelines in this document apply to PHP files unless otherwise noted. References to PHP/HTML files can be interpreted as files that primarily contain HTML, but use PHP for templating purposes.
The keywords "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in RFC 2119.
Most sections are broken up into two parts:
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
如果你想补充内容，建议优先给 free-for-dev 提PR，还能混个高星repo的contributor，没必要加到本列表里。
If you want to make improvements, I would recommend you contributing to free-for-dev rather than this list.
| ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; | |
| ;; | |
| ;; 作者: 李继刚 | |
| ;; 日期: 2025-11-12 | |
| ;; 剑名: 圆桌讨论 | |
| ;; 剑意: 构建一个以“求真”为目标的结构化对话框架。该框架由一位极具洞察力的主持人 | |
| ;; 进行引导，邀请代表不同思想的“典型代表人物”进行一场高强度的、即时响应式的 | |
| ;; 深度对话。主持人将在每轮总结时生成视觉化的思考框架(ASCII Chart)，通过 | |
| ;; “主动质询” 与“协同共建”，对用户提出的议题进行协同探索，最终生成深刻的、 | |
| ;; 结构化的知识网络。 |
Tool: draw.io
Animate the connectors
Animating your connectors is great for demonstrating directional flow charts, electrical circuits and more. To animate your connectors:
This focuses on generating the certificates for loading local virtual hosts hosted on your computer, for development only.
Do not use self-signed certificates in production ! For online certificates, use Let's Encrypt instead (tutorial).
edit /etc/hosts add my.
Bienvenido al curso Java Fundamentals, un programa diseñado para introducirte en los conceptos esenciales de la programación utilizando el lenguaje Java.
Este curso forma parte de una ruta de aprendizaje progresiva y está orientado a desarrollar habilidades básicas en programación estructurada y orientada a objetos.
